1. Field of the Invention
The present invention is related to temporal and spatial data processing.
2. Description of the Related Art
The Global Positioning System is a set of satellites that orbit the earth and make it possible for people with ground GPS receivers to pinpoint their geographic location. The GPS is owned and operated by the U.S. Department of Defense but is available for general use around the world.
At any given time, four satellites from the set of satellites are above the horizon. Each satellite contains a computer, an atomic clock, and a radio. Each satellite has an understanding of its orbit and has a clock. With this, each satellite continually broadcasts its changing position and time. On the ground, each GPS receiver contains a computer that “triangulates” its position by getting bearings from three of the four satellites. The result is a geographic position in the form of a longitude and latitude.
As the GPS receiver continuously triangulates a position, the result is a set of values, referred to as GPS data. Each value provides a longitude and latitude for a time. The GPS data may be stored in a table as “timeseries” data (i.e., timeseries refers to a datatype available in an IBM® Informix® TimeSeries DataBlade® module from International Business Machines Corporation). Timeseries data includes GPS data for a particular type of object (e.g., a car). Moreover, if four or more satellites can be received, the GPS receiver may also include the capability to determine an altitude, in addition to longitude and latitude.
With the proliferation of GPS receivers, more temporal spatial applications are being developed. For example, a temporal application might use timeseries data to identify all stock trades on a company between 4:00 p.m. and 5:00 p.m. A spatial application may use the timeseries data to identify how far a car has traveled between 4:00 p.m. and 5:00 p.m.
Temporal/spatial applications are able to perform many functions, such as: document how many stops a truck makes on a route; determine whether and when an individual's car left a certain location (e.g., a school campus) and whether and when the car returned; determine when a container was loaded onto a ship, where the container stopped while traveling on the ship, and how long before the container arrives at a destination (e.g., a port); determine when connecting flights arrive at an airport; and, determine whether a taxi arrived at a particular location at a particular time.
FIG. 1 illustrates prior art processing by a user to convert time data to spatial data that may be queried with a spatial query. In this example, in FIG. 1, a user desires to submit a Structured Query Language (SQL) request that determines whether a car 100 passed in front of a specific bank 104. Relational DataBase Management System (RDBMS) software uses a Structured Query Language (SQL) interface. The SQL interface has evolved into a standard language for RDBMS software and has been adopted as such by both the American National Standards Institute (ANSI) and the International Standards Organization (ISO).
The car 100 has a GPS receiver that calculates GPS data and a computer system that routes the GPS data to a server (not shown). The user loads the GPS data into a table to create timeseries data 110. Table 1 illustrates a table of timeseries data. The table of timeseries data includes a time, a longitude, and a latitude for each row of the table. The longitude and latitude may be specified in degrees.
TABLE 1TimeLongitudeLatitude00:00:0137 degrees37 degrees. . . . . . . . . 
The user converts the timeseries data 110 through a first SQL conversion 114 into point objects 120 that specify longitude and latitude (i.e., X, Y) values. With the SQL conversion, the user stores the time and point objects 120 into Table 2, which illustrates the data stored after creation of point objects.
TABLE 2TimePoint00:00:01(X, Y). . . . . . 
The user converts the point objects 120 through a second SQL conversion 124 into a line object 130 that reflects a line of travel of the car 100. Table 3 illustrates that a single row of data is stored after a path is generated, and the row has a starting time and a path.
TABLE 3TimePath00:00:01Point1, Point2, . . . 
The line object 130 is a type of spatial object that may be queried with a spatial query 140. Therefore, after creating the line object 130, the user submits spatial query 140 to determine whether the line the car traveled intersects with the building location. Select statement (1) is the spatial query 140 submitted by the user and includes an intersect function. In select statement (1), the user specifies “car.line”, which is the spatial object that the user created.
select intersect(car.line, building.location)(1)  where building.location where build.name = ‘AMB’ and car.id =  ‘taxi’)
Thus, the real time location of the car 100 passes through a number of intermediate steps (i.e., first and second SQL conversions 114, 124) before a spatial query can be made against the time data. In addition, the majority of temporal functionality may be lost in the first SQL conversion 114. This solution stores timeseries data 110 for the car 100 in a database table, and queries (with first SQL conversion 114) the table to build point objects 120. The solution queries the point objects (which are results of the first SQL conversion 114) to generate the path object 130.
Thus, in temporal/spatial applications, data is loaded into a table of timeseries data and selection is done in the temporal domain. That is selection is based on time. The data is converted for use in a spatial domain. Then, analysis of the data and rendering of a spatial object is completed in the spatial domain. In instances in which significant amounts of data are loaded, an IBM® Informix® TimeSeries Real Time Loader (RTL) (available from International Business Machines Corporation) is used to load the data. To facilitate spatial processing, the data points are moved from the time series in the temporal domain to spatial points. The spatial points are then used to build line objects, which are then used for path analysis. This domain translation is time consuming, eroding the value of the timely data, as well as, creating redundancies.
Therefore, there is a need in the art for improved temporal/spatial data processing.